ETL scenario introduction

Here we explore a simple yet realistic scenario. We collect some data regarding traffic, analyze and transform it, then expose the resulting dataset.

Access Jupyter from your Coder instance and create a new notebook. If a Jupyter workspace isn't already available, create one from its template.

Copy the snippets of code from here and paste them in your notebook, then execute them with Shift+Enter. After running, Jupyter will create a new code cell. Alternatively, the final notebook for this scenario can be found in the tutorial repository.

Set-up

First, we initialize our environment and create a project.

Import required libraries:

import digitalhub as dh
import pandas as pd
import requests
import os

Create a project:

PROJECT = "demo-etl"
project = dh.get_or_create_project(PROJECT)

Check that the project has been created successfully:

print(project)

Peek at the data

Let's take a look at the data we will work with, which is available in CSV (Comma-Separated Values) format at a remote API.

Set the URL to the data and the file name:

URL = "https://opendata.comune.bologna.it/api/explore/v2.1/catalog/datasets/rilevazione-flusso-veicoli-tramite-spire-anno-2023/exports/csv?lang=it&timezone=Europe%2FRome&use_labels=true&delimiter=%3B"
filename = "rilevazione-flusso-veicoli-tramite-spire-anno-2023.csv"

Download the file and save it locally:

with requests.get(URL) as r:
    with open(filename, "wb") as f:
        f.write(r.content)

Use pandas to read the file into a dataframe:

df = pd.read_csv(filename, sep=";")

You can now run df.head() to view the first few records of the dataset. They contain information about how many vehicles have passed a sensor (spire), located at specific coordinates, within different time slots. If you wish, use df.dtypes to list the columns and respective types of the data, or df.size to know the data's size in Bytes.

In the next section, we will collect this data and save it to the object store.